Exploring dual-attention mechanism with multi-scale feature extraction scheme for skin lesion segmentation

Automatic segmentation of skin lesions from dermoscopic images is a challenging task due to the irregular lesion boundaries, poor contrast between the lesion and the background, and the presence of artifacts. In this work, a new convolutional neural network-based approach is proposed for skin lesion segmentation. In this work, a novel multi-scale feature extraction module is proposed for extracting more discriminative features for dealing with the challenges related to complex skin lesions; this module is embedded in the UNet, replacing the convolutional layers in the standard architecture. Further in this work, two different attention mechanisms refine the feature extracted by the encoder and the post-upsampled features. This work was evaluated using the two publicly available datasets, including ISBI2017 and ISIC2018 datasets. The proposed method reported an accuracy, recall, and JSI of 97.5%, 94.29%, 91.16% on the ISBI2017 dataset and 95.92%, 95.37%, 91.52% on the ISIC2018 dataset. It outperformed the existing methods and the top-ranked models in the respective competitions.

Course Difficulty Estimation Based on Mapping of Bloom's Taxonomy and ABET Criteria

Current Educational system uses grades or marks to assess the performance of the student. The marks or grades a students scores depends on different parameters, the main parameter being the difficulty level of a course. Computation of this difficulty level may serve as a support for both the students and teachers to fix the level of training needed for successful completion of course. In this paper, we proposed a methodology that estimates the difficulty level of a course by mapping the Bloom's Taxonomy action words along with Accreditation Board for Engineering and Technology (ABET) criteria and learning outcomes. The estimated difficulty level is validated based on the history of grades secured by the students.

Class dependency based learning using Bi-LSTM coupled with the transfer learning of VGG16 for the diagnosis of Tuberculosis from chest x-rays

Tuberculosis is an infectious disease that is leading to the death of millions of people across the world. The mortality rate of this disease is high in patients suffering from immuno-compromised disorders. The early diagnosis of this disease can save lives and can avoid further complications. But the diagnosis of TB is a very complex task. The standard diagnostic tests still rely on traditional procedures developed in the last century. These procedures are slow and expensive. So this paper presents an automatic approach for the diagnosis of TB from posteroanterior chest x-rays. This is a two-step approach, where in the first step the lung regions are segmented from the chest x-rays using the graph cut method, and then in the second step the transfer learning of VGG16 combined with Bi-directional LSTM is used for extracting high-level discriminative features from the segmented lung regions and then classification is performed using a fully connected layer. The proposed model is evaluated using data from two publicly available databases namely Montgomery Country set and Schezien set. The proposed model achieved accuracy and sensitivity of 97.76%, 97.01% and 96.42%, 94.11% on Schezien and Montgomery county datasets. This model enhanced the diagnostic accuracy of TB by 0.7% and 11.68% on Schezien and Montgomery county datasets.

Machine Learning and Deep Learning Methods for Building Intelligent Systems in Medicine and Drug Discovery: A Comprehensive Survey

With the advancements in computer technology, there is a rapid development of intelligent systems to understand the complex relationships in data to make predictions and classifications. Artificail Intelligence based framework is rapidly revolutionizing the healthcare industry. These intelligent systems are built with machine learning and deep learning based robust models for early diagnosis of diseases and demonstrates a promising supplementary diagnostic method for frontline clinical doctors and surgeons. Machine Learning and Deep Learning based systems can streamline and simplify the steps involved in diagnosis of diseases from clinical and image-based data, thus providing significant clinician support and workflow optimization. They mimic human cognition and are even capable of diagnosing diseases that cannot be diagnosed with human intelligence. This paper focuses on the survey of machine learning and deep learning applications in across 16 medical specialties, namely Dental medicine, Haematology, Surgery, Cardiology, Pulmonology, Orthopedics, Radiology, Oncology, General medicine, Psychiatry, Endocrinology, Neurology, Dermatology, Hepatology, Nephrology, Ophthalmology, and Drug discovery. In this paper along with the survey, we discuss the advancements of medical practices with these systems and also the impact of these systems on medical professionals.